Introduction RNA-sequencing Data Analysis
W. Evan Johnson, Ph.D., Professor, Division of Infectious Disease, Director, Center for Data Science, Rutgers University – New Jersey Medical School
2023-11-17
Objective
- Disclaimer: non-comprehensive introduction to RNA-sequencing
- Introduce preprocessing steps
- Visualization
- Analytical methods
- Common software tools
Steps to an RNA-seq Analysis (Literacy)
- Preprocessing and QC:
- Fasta and Fastq files
- FastQC: good vs. bad examples
- Visualization
- Alignment
- Obtaining genome sequence and annotation
- Software: Bowtie, TopHat, STAR, Subread/Rsubread
- Expression Quantification
- Count reads hitting genes, etc
- Approaches/software: HT-Seq, STAR, Cufflinks, RPKM FPKM or CPM, RSEM, edgeR, findOverlaps (GenomicRanges). featureCounts (Rsubread)
- More visualization
- Heatmaps, boxplots, PCA, t-SNE, UMAP
- Differential Expression
- Batch correction
- Overdispersion
- General Workflow
- Available tools: edgeR, DESeq, Limma/voom
- Even more visualization!!
Illumina Sequencing Workflow
Figure 1: Illumina Sequencing Workflow
Sequencing Data Formats
Genome sequcencing data is often stored in one of two formats, FASTA and FASTQ text files. For example a FASTA files looks like the following:
FASTA Files
Figure 2: FASTA file format
FASTQ Files
We can also store confidence or quality scores using a FASTQ format:
FASTQ Encoding
In order to translate FASTQ quality scores:
Figure 4: Fastq Encoding
FASTQ Probability
And now converting to confidence probabilities:
Figure 5: Fastq encoding quality scores
Preprocessing and QC using FASTQC
FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) provides a simple way to do QC checks on raw sequence data:
- Import of data from BAM, SAM or FastQ files
- Quick overview and summary graphs and tables to quickly assess your data
- Export of results to an HTML based permanent report
- Offline operation to allow automated generation of reports without running the interactive application
To rund FastQC you can launch the GUI app, or run form the command line:
rna_seq_files/FastQC/./fastqc rna_seq_files/reads/R01_10_short500K.fq.gz## Started analysis of R01_10_short500K.fq.gz
## Approx 5% complete for R01_10_short500K.fq.gz
## Approx 10% complete for R01_10_short500K.fq.gz
## Approx 15% complete for R01_10_short500K.fq.gz
## Approx 20% complete for R01_10_short500K.fq.gz
## Approx 25% complete for R01_10_short500K.fq.gz
## Approx 30% complete for R01_10_short500K.fq.gz
## Approx 35% complete for R01_10_short500K.fq.gz
## Approx 40% complete for R01_10_short500K.fq.gz
## Approx 45% complete for R01_10_short500K.fq.gz
## Approx 50% complete for R01_10_short500K.fq.gz
## Approx 55% complete for R01_10_short500K.fq.gz
## Approx 60% complete for R01_10_short500K.fq.gz
## Approx 65% complete for R01_10_short500K.fq.gz
## Approx 70% complete for R01_10_short500K.fq.gz
## Approx 75% complete for R01_10_short500K.fq.gz
## Approx 80% complete for R01_10_short500K.fq.gz
## Approx 85% complete for R01_10_short500K.fq.gz
## Approx 90% complete for R01_10_short500K.fq.gz
## Approx 95% complete for R01_10_short500K.fq.gz
## Analysis complete for R01_10_short500K.fq.gzFastQC Scores
Figure 5: Fastqc scores
FastQC Score Distribution
Figure 6: Fastqc score distribution
FastQC Base Distribution
Figure 7: Fastqc base distribution
FastQC N Distribution
Figure 8: Fastqc N distribution
Alignment to the Reference Genome
Find the genomic Location of origin for the sequencing read. Software: Bowtie2, TopHat, STAR, Subread/Rsubread, many others!
Figure 9: Sequence read alignment
Here is quick tutorial on sequnce aligment.
Using Rsubread to do Alignment
The following userguide will be helpful for you:
http://bioinf.wehi.edu.au/subread-package/SubreadUsersGuide.pdf
Indexing your genome
Abraham Lincoln: “Give me six hours to chop down a tree and I will spend the first four sharpening the axe.” (4 minutes indexing the genome, 2 minutes aligning the reads)
Note that you will rarely do this for human alignment. You will usually download an existing index given to you by others who have already done this work. You will do this often if you are aligning microbial reads, e.g. MTB or some other organism for which others have not already made your index for you.
buildindex(basename="rna_seq_files/genome/ucsc.hg19.chr1_120-150M",reference="rna_seq_files/genome/ucsc.hg19.chr1_120-150M.fasta.gz")##
## ========== _____ _ _ ____ _____ ______ _____
## ===== / ____| | | | _ \| __ \| ____| /\ | __ \
## ===== | (___ | | | | |_) | |__) | |__ / \ | | | |
## ==== \___ \| | | | _ <| _ /| __| / /\ \ | | | |
## ==== ____) | |__| | |_) | | \ \| |____ / ____ \| |__| |
## ========== |_____/ \____/|____/|_| \_\______/_/ \_\_____/
## Rsubread 2.16.0
##
## //================================= setting ==================================\\
## || ||
## || Index name : ucsc.hg19.chr1_120-150M ||
## || Index space : base space ||
## || Index split : no-split ||
## || Repeat threshold : 100 repeats ||
## || Gapped index : no ||
## || ||
## || Free / total memory : 5.8GB / 24.0GB ||
## || ||
## || Input files : 1 file in total ||
## || o ucsc.hg19.chr1_120-150M.fasta.gz ||
## || ||
## \\============================================================================//
##
## //================================= Running ==================================\\
## || ||
## || Check the integrity of provided reference sequences ... ||
## || There were 4800000 notes for reference sequences. ||
## || The notes can be found in the log file, 'rna_seq_files/genome/ucsc.hg1 ... ||
## || Scan uninformative subreads in reference sequences ... ||
## || 516 uninformative subreads were found. ||
## || These subreads were excluded from index building. ||
## || Estimate the index size... ||
## || 8%, 0 mins elapsed, rate=15954.1k bps/s ||
## || 16%, 0 mins elapsed, rate=22222.5k bps/s ||
## || 24%, 0 mins elapsed, rate=25552.5k bps/s ||
## || 33%, 0 mins elapsed, rate=27655.8k bps/s ||
## || 41%, 0 mins elapsed, rate=29070.7k bps/s ||
## || 49%, 0 mins elapsed, rate=30048.3k bps/s ||
## || 58%, 0 mins elapsed, rate=30829.2k bps/s ||
## || 66%, 0 mins elapsed, rate=31455.2k bps/s ||
## || 74%, 0 mins elapsed, rate=31959.7k bps/s ||
## || 83%, 0 mins elapsed, rate=29181.2k bps/s ||
## || 91%, 0 mins elapsed, rate=26588.8k bps/s ||
## || 3.0 GB of memory is needed for index building. ||
## || Build the index... ||
## || 8%, 0 mins elapsed, rate=2378.5k bps/s ||
## || 16%, 0 mins elapsed, rate=4057.5k bps/s ||
## || 24%, 0 mins elapsed, rate=5347.1k bps/s ||
## || 33%, 0 mins elapsed, rate=6347.1k bps/s ||
## || 41%, 0 mins elapsed, rate=7103.7k bps/s ||
## || 49%, 0 mins elapsed, rate=7722.0k bps/s ||
## || 58%, 0 mins elapsed, rate=8194.3k bps/s ||
## || 66%, 0 mins elapsed, rate=8653.7k bps/s ||
## || 74%, 0 mins elapsed, rate=9061.5k bps/s ||
## || 83%, 0 mins elapsed, rate=8794.2k bps/s ||
## || 91%, 0 mins elapsed, rate=8357.6k bps/s ||
## || Save current index block... ||
## || [ 0.0% finished ] ||
## || [ 10.0% finished ] ||
## || [ 20.0% finished ] ||
## || [ 30.0% finished ] ||
## || [ 40.0% finished ] ||
## || [ 50.0% finished ] ||
## || [ 60.0% finished ] ||
## || [ 70.0% finished ] ||
## || [ 80.0% finished ] ||
## || [ 90.0% finished ] ||
## || [ 100.0% finished ] ||
## || ||
## || Total running time: 0.3 minutes. ||
## || Index rna_seq_files/genome/ucsc.hg19.chr1_120-150M was successfully built. ||
## || ||
## \\============================================================================//Took me ~0.2 minutes
Aligning your reads:
Note that this outputs results in a .bam file and not a .sam file
align(index="rna_seq_files/genome/ucsc.hg19.chr1_120-150M",readfile1="rna_seq_files/reads/R01_10_short500K.fq.gz",output_file="rna_seq_files/alignments/R01_10_short.bam", nthreads=4) ##
## ========== _____ _ _ ____ _____ ______ _____
## ===== / ____| | | | _ \| __ \| ____| /\ | __ \
## ===== | (___ | | | | |_) | |__) | |__ / \ | | | |
## ==== \___ \| | | | _ <| _ /| __| / /\ \ | | | |
## ==== ____) | |__| | |_) | | \ \| |____ / ____ \| |__| |
## ========== |_____/ \____/|____/|_| \_\______/_/ \_\_____/
## Rsubread 2.16.0
##
## //================================= setting ==================================\\
## || ||
## || Function : Read alignment (RNA-Seq) ||
## || Input file : R01_10_short500K.fq.gz ||
## || Output file : R01_10_short.bam (BAM) ||
## || Index name : ucsc.hg19.chr1_120-150M ||
## || ||
## || ------------------------------------ ||
## || ||
## || Threads : 4 ||
## || Phred offset : 33 ||
## || Min votes : 3 / 10 ||
## || Max mismatches : 3 ||
## || Max indel length : 5 ||
## || Report multi-mapping reads : yes ||
## || Max alignments per multi-mapping read : 1 ||
## || ||
## \\============================================================================//
##
## //================ Running (16-Nov-2023 17:01:48, pid=88194) =================\\
## || ||
## || Check the input reads. ||
## || The input file contains base space reads. ||
## || Initialise the memory objects. ||
## || Estimate the mean read length. ||
## || The range of Phred scores observed in the data is [2,37] ||
## || Create the output BAM file. ||
## || Check the index. ||
## || Init the voting space. ||
## || Global environment is initialised. ||
## || Load the 1-th index block... ||
## || The index block has been loaded. ||
## || Start read mapping in chunk. ||
## || 5% completed, 2.0 mins elapsed, rate=62.5k reads per second ||
## || 11% completed, 2.0 mins elapsed, rate=90.1k reads per second ||
## || 18% completed, 2.0 mins elapsed, rate=105.6k reads per second ||
## || 24% completed, 2.0 mins elapsed, rate=118.4k reads per second ||
## || 31% completed, 2.0 mins elapsed, rate=125.8k reads per second ||
## || 38% completed, 2.0 mins elapsed, rate=133.3k reads per second ||
## || 44% completed, 2.0 mins elapsed, rate=137.2k reads per second ||
## || 51% completed, 2.0 mins elapsed, rate=140.1k reads per second ||
## || 58% completed, 2.0 mins elapsed, rate=143.3k reads per second ||
## || 64% completed, 2.0 mins elapsed, rate=146.5k reads per second ||
## || 69% completed, 2.0 mins elapsed, rate=2.9k reads per second ||
## || 73% completed, 2.0 mins elapsed, rate=3.0k reads per second ||
## || 76% completed, 2.0 mins elapsed, rate=3.1k reads per second ||
## || 80% completed, 2.0 mins elapsed, rate=3.3k reads per second ||
## || 83% completed, 2.0 mins elapsed, rate=3.4k reads per second ||
## || 86% completed, 2.0 mins elapsed, rate=3.6k reads per second ||
## || 90% completed, 2.1 mins elapsed, rate=3.7k reads per second ||
## || 93% completed, 2.1 mins elapsed, rate=3.8k reads per second ||
## || 96% completed, 2.1 mins elapsed, rate=4.0k reads per second ||
## || ||
## || Completed successfully. ||
## || ||
## \\==================================== ====================================//
##
## //================================ Summary =================================\\
## || ||
## || Total reads : 503,568 ||
## || Mapped : 376,964 (74.9%) ||
## || Uniquely mapped : 317,555 ||
## || Multi-mapping : 59,409 ||
## || ||
## || Unmapped : 126,604 ||
## || ||
## || Indels : 2,236 ||
## || ||
## || Running time : 2.1 minutes ||
## || ||
## \\============================================================================//## R01_10_short.bam
## Total_reads 503568
## Mapped_reads 376964
## Uniquely_mapped_reads 317555
## Multi_mapping_reads 59409
## Unmapped_reads 126604
## Indels 2236My laptop is an Apple M2, which has 8 cores (used 4 cores), 24GB RAM
Took 15.7 minutes to align ~60M reads to the 30M bases
Took 0.7 minutes to align ~6.5M reads to the 30M bases
Took 0.3 minutes to align ~500K reads to the 30M bases
Aligned Sequencing Data Formats (SAM and BAM)
Note that Rsubread outputs a .bam file (bam = binary alignment map) and not a .sam file (sam = sequence alignment map). Here is some information about a .sam file:
https://en.wikipedia.org/wiki/SAM_(file_format)
Figure 10: SAM and BAM file format
https://samtools.github.io/hts-specs/SAMv1.pdf
To convert .sam to .bam or vice versa, a package called Rsamtools. Using Rsamtools, you can convert bam to sam as follows:
asSam("rna_seq_files/alignments/R01_10_short.bam", overwrite=T) ## [1] "rna_seq_files/alignments/R01_10_short.sam"# To convert to bam:
#asBam("rna_seq_files/alignments/R01_10_short.bam") Makes a system call to the Mac terminal to generate a .sam file
Feature counts
Now we can count reads hitting genes. Approaches/software:
- HT-Seq
- STAR
- Cufflinks
- RPKM FPKM or CPM
- RSEM
- edgeR
- findOverlaps (GenomicRanges)
- featureCounts (Rsubread)
Figure 11: Feature Counts
fCountsList = featureCounts("rna_seq_files/alignments/R01_10_short.bam", annot.ext="rna_seq_files/genome/genes.chr1_120-150M.gtf", isGTFAnnotationFile=TRUE)##
## ========== _____ _ _ ____ _____ ______ _____
## ===== / ____| | | | _ \| __ \| ____| /\ | __ \
## ===== | (___ | | | | |_) | |__) | |__ / \ | | | |
## ==== \___ \| | | | _ <| _ /| __| / /\ \ | | | |
## ==== ____) | |__| | |_) | | \ \| |____ / ____ \| |__| |
## ========== |_____/ \____/|____/|_| \_\______/_/ \_\_____/
## Rsubread 2.16.0
##
## //========================== featureCounts setting ===========================\\
## || ||
## || Input files : 1 BAM file ||
## || ||
## || R01_10_short.bam ||
## || ||
## || Paired-end : no ||
## || Count read pairs : no ||
## || Annotation : genes.chr1_120-150M.gtf (GTF) ||
## || Dir for temp files : . ||
## || Threads : 1 ||
## || Level : meta-feature level ||
## || Multimapping reads : counted ||
## || Multi-overlapping reads : not counted ||
## || Min overlapping bases : 1 ||
## || ||
## \\============================================================================//
##
## //================================= Running ==================================\\
## || ||
## || Load annotation file genes.chr1_120-150M.gtf ... ||
## || Features : 1918 ||
## || Meta-features : 104 ||
## || Chromosomes/contigs : 1 ||
## || ||
## || Process BAM file R01_10_short.bam... ||
## || Single-end reads are included. ||
## || Total alignments : 503568 ||
## || Successfully assigned alignments : 4561 (0.9%) ||
## || Running time : 0.01 minutes ||
## || ||
## || Write the final count table. ||
## || Write the read assignment summary. ||
## || ||
## \\============================================================================//featureCounts = cbind(fCountsList$annotation[,1], fCountsList$counts)
write.table(featureCounts, "rna_seq_files/alignments/R01_10_short.features.txt", sep="\t", col.names=FALSE, row.names=FALSE, quote=FALSE)SCTK
Use the Single Cell Toolkit to analyze your RNA-seq data!
- Inputs: RNA-seq, Nanostring, Proteomic, immunological assay data
- Interactive analyses and visualization of data
- Save results, figures, etc
- Sophisticated data structures
- R/Bioconductor package
Figure 12: Feature Counts
Figure 13: Feature Counts
#install.packages("devtools")
#devtools::install_github("wevanjohnson/singleCellTK")
library(singleCellTK)
singleCellTK()
### open features_combined.txt
### and meta_data.txtTopics in downstream analyses:
Batch effects
Batch Effect: Non-biological variation due to differences in batches of data that confound the relationships between covariates of interest.
Batch effects are caused by differences in:
- Gene expression profiling platform
- Lab protocol or experimenter
- Time of day or processing
- Atmospheric ozone level (Rhodes et al. 2004)
Batch Effect Examples:
Example #1: Nirtic Oxide
Example 1 resulted from an oligonucleotide microarray (Affymetrix HG-U133A) experiment on human lung fibroblast cells (IMR90) designed to reveal whether exposing mammalian cells to nitric oxide (NO) stabilizes mRNAs. Control samples and samples exposed to NO for 1 h were then transcription inhibited for 7.5 h. Microarray data were collected at baseline (0 h, just before transcription inhibition) and at the end of the experiment (after 7.5 h) for both the control and the NO-treated group. It was hypothesized that NO will induce or inhibit the expression of some genes, but would also stabilize the mRNA of many genes, preventing them from being degraded after 7.5 h.
Figure 14
Example #2: Control Gene Expression
Figure 15
Example #3: Proteomic markers to predict endometriosis (39 total)
Single peptide predictors of disease (AUC): 0.82, 0.76, 0.74, 0.74, 0.70 (+12 more >0.6)
Single peptide predictors of batch (AUC): 0.99, 0.94, 0.91, 0.86, 0.86, 0.84, 0.84, 0.84, 0.83, 0.82 (+7 more >0.6)
Predict batch better than disease!
ComBat Batch Adjustment
Normalization
Need to normalize data because of:
- Sequencing depth difference in each RNA sample
- RNA composition differences
- Highly expressed genes can consume a substantial proportion of RNA-Seq reads, causing other genes to be under-sampled
- Different methods
- Log counts
- Counts per million (CPM and logCPM; RPKM, FPKM)
- Trimmed mean of M-values (edgeR/limma)
- Median of Ratios method (DESeq)
Figure 18
Problem of overdispersion:
Alignment and feature counting result in discrete count data (i.e. the number of reads to each gene). A first thought might be to use a Poisson distribution to model the counts. However, the Poisson makes a strict mean-variance assumption (i.e. they are the same. Studies have demonstrated that a negative binomial fits data better.
Figure 19
Data Structures
A data structure is a particular way of organizing data in a computer so that it can be used effectively. The idea is to reduce the space and time complexities of different tasks.
Data structures in R programming are tools for holding multiple values, variables, and sometimes functions
Please think very carefully about the way you manage and store your data! This can make your life much easier and make your code and data cleaner and more portable!
There are advanced R data structures, S3 and
S4 class objects, that can facilitate object orientated
programming. One useful example of an S4 class data structure is the
SummarizedExperiment object. 
Visualization and Dimension reduction
Read in dataset
Dataset from: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-018-3127-4
counts <- read.table("rna_seq_files/downstream_analysis/features_combined.txt", sep="\t",
header=T, row.names=1)
meta_data <- read.table("rna_seq_files/downstream_analysis/meta_data.txt", sep="\t",
header=T, row.names=1)
group <- meta_data$Disease
sce_hivtb <- SummarizedExperiment(assays=list(counts=counts),
colData = meta_data)
sce_hivtb <- mkAssay(sce_hivtb, log = TRUE, counts_to_CPM = TRUE)
assays(sce_hivtb)## List of length 4
## names(4): counts log_counts counts_cpm log_counts_cpmPCA
set.seed(1)
pca_out <- prcomp(t(assay(sce_hivtb,"log_counts_cpm")))
pca_plot <- as.data.frame(pca_out$x)
pca_plot$Disease <- as.factor(sce_hivtb$Disease)
g <- pca_plot %>% ggplot(aes(x=PC1, y=PC2, color=Disease)) +
geom_point(size=1.5) + xlab("PCA1") + ylab("PCA2") +
theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("PCA Plot")
plot(g)
UMAP
set.seed(1)
umap_out <- umap(t(assay(sce_hivtb,"log_counts_cpm")))
umap_plot <- as.data.frame(umap_out$layout)
umap_plot$Disease <- as.factor(sce_hivtb$Disease)
g <- umap_plot %>% ggplot(aes(x=V1, y=V2, color=Disease)) +
geom_point(size=1.5) + xlab("UMAP1") + ylab("UMAP2") +
theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("UMAP Plot")
plot(g)
Differential Expression
Figure 21
EdgeR Example
Implements statistical methods for DE analysis based on the negative binomial model:
counts<-counts[which(rowSums(cpm(counts))>1),] #Gene Filtering
dge <- DGEList(counts=counts, group=group) #Computes library size
dge <- calcNormFactors(dge) #TMM normalization
design<-model.matrix(~group)
dge<-estimateDisp(counts,design) #Estimates common, trended and tagwise dispersionIn negative binomial models, each gene is given a dispersion parameter. Dispersions control the variances of the gene counts and underestimation will lead to false discovery and overestimation may lead to a lower rate of true discovery
#perform likelihood ratio tests
fit<-glmFit(counts,design, dispersion=dge$tagwise.dispersion) #fits a negative binomial GLM with the dispersion estimates
lrt<-glmLRT(fit, coef=2) #Performs likelihood ratio test, comparing the goodness of the fit of the full versus reduced model
topTags(lrt)## Coefficient: grouptb_hiv
## logFC logCPM LR PValue FDR
## IL1R2 4.334196 8.207931 100.01344 1.513665e-23 2.933634e-19
## AP3B2 5.758193 2.952770 71.58586 2.654527e-17 2.572370e-13
## FCGR1C 2.818498 4.536149 65.07230 7.220003e-16 4.664362e-12
## VNN1 3.150158 8.071776 64.39089 1.020287e-15 4.943545e-12
## CYP1B1 3.135471 6.873000 63.00698 2.059751e-15 7.984006e-12
## IL18R1 2.726131 6.487474 60.75863 6.451968e-15 2.084093e-11
## SLC29A1 -4.135274 3.969739 59.69166 1.109455e-14 3.071764e-11
## CACNG8 -4.134373 3.189823 58.97316 1.598376e-14 3.872266e-11
## SOCS3 2.665230 6.475922 57.71505 3.029759e-14 6.524418e-11
## ZAK 2.601746 6.126750 56.08435 6.942749e-14 1.345574e-10#perform quasi-likelihood F-tests
#Replace the chisquare approximation to the likelihood ratio statistic with a quasi-likelihood F-test, more control of error rate
fit<-glmQLFit(counts, design, dispersion=dge$tagwise.dispersion) #use for small dataset, reflects uncertainty in estimating dispersion for each gene, more robust and reliable error rate #control when the number of replicates is small
qlf<-glmQLFTest(fit, coef=2)
topTags(qlf)## Coefficient: grouptb_hiv
## logFC logCPM F PValue FDR
## IL1R2 4.334196 8.207931 98.25774 3.688811e-23 7.149284e-19
## AP3B2 5.758193 2.952770 68.34333 1.376533e-16 1.333930e-12
## FCGR1C 2.818498 4.536149 63.94549 1.281415e-15 8.278371e-12
## VNN1 3.150158 8.071776 63.35564 1.728679e-15 8.375881e-12
## CYP1B1 3.135471 6.873000 62.52984 2.628931e-15 1.019026e-11
## IL18R1 2.726131 6.487474 60.35328 7.940072e-15 2.564775e-11
## SLC29A1 -4.135274 3.969739 58.17236 2.404908e-14 6.658504e-11
## SOCS3 2.665230 6.475922 57.33072 3.688947e-14 8.936935e-11
## CACNG8 -4.134373 3.189823 56.59826 5.353531e-14 1.152853e-10
## ZAK 2.601746 6.126750 55.70915 8.414232e-14 1.630762e-10#For visualization, heatmaps/PCA
Logcpm<-cpm(counts,log=TRUE)DESeq2 Example
dds <- DESeqDataSetFromMatrix(countData = counts, colData=meta_data, design=~Disease)## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors#colData is a data frame of demographic/phenotypic data
dds<-dds[rowSums(counts(dds))>1,] #Gene Filtering
dds<-DESeq(dds) #Performs estimation of size factors,dispersion, and negative binomial GLM f#itting## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 180 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingres <- results(dds)[order(results(dds)[,6]),]
res[1:10,]## log2 fold change (MLE): Disease tb hiv art vs hiv only
## Wald test p-value: Disease tb hiv art vs hiv only
## DataFrame with 10 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ZEB2 1702.8164 1.75543 0.217662 8.06493 7.32802e-16 1.19996e-11
## DCUN1D3 48.7029 2.52495 0.361657 6.98163 2.91765e-12 2.38883e-08
## TIPARP 724.9429 2.22978 0.334020 6.67558 2.46254e-11 1.34414e-07
## ITPKC 196.8723 3.04144 0.465638 6.53177 6.49981e-11 2.66086e-07
## LAIR1 1528.6852 3.01392 0.472158 6.38328 1.73337e-10 4.40512e-07
## COPS4 559.7527 2.67612 0.419911 6.37306 1.85294e-10 4.40512e-07
## IGF2BP3 360.1066 3.29761 0.517782 6.36873 1.90604e-10 4.40512e-07
## GSAP 964.2702 1.26506 0.199220 6.35007 2.15212e-10 4.40512e-07
## RBMS1 3297.7791 2.51612 0.400420 6.28370 3.30604e-10 6.01516e-07
## ZDHHC20 554.9810 2.51989 0.403490 6.24523 4.23175e-10 6.92949e-07Heatmap of DEGs
# Make a Heatmap of DEGs
mat = as.matrix(assay(sce_hivtb,"log_counts_cpm"))[order(results(dds)[,6])[1:100],] # Using first 1000 genes to simplify
mat = t(scale(t(mat)))
df=data.frame(Disease=colData(sce_hivtb)$Disease)
ha = HeatmapAnnotation(df = df, col = list(Disease=c("tb_hiv"="Red","hiv_only"="Blue", "tb_hiv_art"="Green")))
Heatmap(mat,show_row_names=F,show_column_names = F, top_annotation = ha)
Limma Example
- Most similar to microarray data flow
- Reads counts are converted to log2 counts per million (logCPM) and the mean-variance relationship is modeled with precision weights (voom transform)
dge <- DGEList(counts=counts, group=group) #From edgeR, Computes library size
counts<-counts[which(rowSums(cpm(counts))>1),] #Gene Filtering
dge <- DGEList(counts=counts, group=group) #Re-compute library size
dge <- calcNormFactors(dge) #TMM normalization
design<-model.matrix(~group)
v<-voom(dge, design) #voom transform to calculate weights to eliminate mean-variance #relationship
#use usual limma pipelines
fit<-lmFit(v,design)
fit<-eBayes(fit)
topTable(fit, coef=ncol(design))## logFC AveExpr t P.Value adj.P.Val B
## DCUN1D3 2.415048 1.1213257 7.597067 5.330234e-09 5.174623e-05 10.335712
## ZEB2 1.681109 6.4208329 7.439069 8.537472e-09 5.174623e-05 10.090462
## LINC01093 5.474634 0.8710200 7.414057 9.200542e-09 5.174623e-05 9.932749
## FAM151B 3.709561 1.2894208 7.364262 1.067978e-08 5.174623e-05 9.825077
## C7orf61 2.599925 2.7347797 7.206753 1.714091e-08 5.536801e-05 9.443534
## CYP19A1 6.863347 -0.9439843 7.234226 1.578062e-08 5.536801e-05 9.241797
## TIPARP 2.118756 5.0816090 6.625677 9.994987e-08 1.885771e-04 7.773090
## IGF2BP3 3.336976 3.6439397 6.618279 1.022347e-07 1.885771e-04 7.751390
## COL4A2-AS1 5.463005 -3.3142037 6.975753 3.444177e-08 9.535941e-05 7.614380
## LAYN 3.342889 -0.9931241 6.639498 9.581743e-08 1.885771e-04 7.409620Pathway analysis
After finding DEGs, look for correlated genes/networks and enriched pathway sets in the gene set using:
- Weighted gene coexpression network analysis (WGCNA)
- GSEA, GSVA, EnrichR, many more!!
- Qiagen Ingenuity Pathway Analysis (IPA)
Figure 22
Figure 23
TBSignatureProfiler Analysis
The TBSignatureProfiler was developed in the Johnson Lab in 2021 to profile new and existing TB gene expression signatures https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-020-05598-z
sce_hivtb_2 <- sce_hivtb[,colData(sce_hivtb)$Disease != "tb_hiv_art"]
TBsigs <- TBsignatures[-12]
ssgsea_res <- runTBsigProfiler(sce_hivtb_2, useAssay = "log_counts_cpm",
signatures = TBsigs,
algorithm = "ssGSEA",
combineSigAndAlgorithm = TRUE,
parallel.sz = 1)## Warning: Calling gsva(expr=., gset.idx.list=., method=., ...) is deprecated;
## use a method-specific parameter object (see '?gsva').## Warning in .filterFeatures(expr, method): 2204 genes with constant expression
## values throughout the samples.Signature Heatmap
# Colors for gradient
signatureHeatmap(ssgsea_res, name = "Heatmap of Signatures (ssGSEA)",
signatureColNames = names(TBsigs),
annotationColNames = c("Disease"),
scale = TRUE,
split_heatmap = "none",
showColumnNames = FALSE)
Signature Boxplots
signatureBoxplot(ssgsea_res, name="ssGSEA",
signatureColNames = names(TBsigs),
annotationColName = c("Disease"))## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.
## Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Boxplots Single
for (i in names(TBsigs)){
cat("#####" , i, "\n")
print(signatureBoxplot(ssgsea_res, name=i, signatureColNames = i,
annotationColName = c("Disease")))
cat("\n\n")
}Anderson_42
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.
## Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Anderson_OD_51
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Berry_393
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Berry_OD_86
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Blankley_380
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Blankley_5
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Bloom_OD_144
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Bloom_RES_268
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Bloom_RES_558
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Chen_5
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Chen_HIV_4
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Darboe_RISK_11
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Dawany_HIV_251
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Duffy_23
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Esmail_203
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Esmail_82
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Esmail_OD_893
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Estevez_133
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Estevez_259
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Francisco_OD_2
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Gjoen_10
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Gjoen_7
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Gliddon_2_OD_4
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Gliddon_HIV_3
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Gliddon_OD_3
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Gliddon_OD_4
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Gong_OD_4
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Heycken_FAIL_22
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Hoang_OD_13
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Hoang_OD_20
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Hoang_OD_3
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Huang_OD_13
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Jacobsen_3
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Jenum_8
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Kaforou_27
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Kaforou_OD_44
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Kaforou_OD_53
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Kaul_3
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Kulkarni_HIV_2
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Kwan_186
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
LauxdaCosta_OD_3
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Lee_4
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Leong_24
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Leong_RISK_29
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Long_RES_10
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Maertzdorf_15
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Maertzdorf_4
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Maertzdorf_OD_100
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Natarajan_7
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
PennNich_RISK_6
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Qian_OD_17
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Rajan_HIV_5
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Roe_3
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Roe_OD_4
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Sambarey_HIV_10
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Singhania_OD_20
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Sivakumaran_11
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Sloot_HIV_2
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Suliman_4
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Suliman_RISK_2
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Suliman_RISK_4
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Sweeney_OD_3
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Tabone_OD_11
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Tabone_RES_25
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Tabone_RES_27
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Thompson_9
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Thompson_FAIL_13
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Thompson_RES_5
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Tornheim_71
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Tornheim_RES_25
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Vargas_18
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Vargas_42
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Verhagen_10
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Walter_51
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Walter_PNA_119
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Walter_PNA_47
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Zak_RISK_16
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Zhao_NANO_6
## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.## Warning: Use of `boxplotdfm$Group` is discouraged.
## ℹ Use `Group` instead.## Warning: Use of `boxplotdfm$Score` is discouraged.
## ℹ Use `Score` instead.
Signature Gene Heatmaps
for (i in names(TBsigs)){
cat("#####" , i, "\n")
signatureGeneHeatmap(ssgsea_res, useAssay="log_counts_cpm",
TBsigs[[i]],
name = i, signatureColNames = NULL,
annotationColNames = c(i,"Disease"),
showColumnNames = FALSE)
cat("\n\n")
}Anderson_42
Anderson_OD_51
Berry_393
Berry_OD_86
Blankley_380
Blankley_5
Bloom_OD_144
Bloom_RES_268
Bloom_RES_558
Chen_5
Chen_HIV_4
Darboe_RISK_11
Dawany_HIV_251
Duffy_23
Esmail_203
Esmail_82
Esmail_OD_893
Estevez_133
Estevez_259
Francisco_OD_2
Gjoen_10
Gjoen_7
Gliddon_2_OD_4
Gliddon_HIV_3
Gliddon_OD_3
Gliddon_OD_4
Gong_OD_4
Heycken_FAIL_22
Hoang_OD_13
Hoang_OD_20
Hoang_OD_3
Huang_OD_13
Jacobsen_3
Jenum_8
Kaforou_27
Kaforou_OD_44
Kaforou_OD_53
Kaul_3
Kulkarni_HIV_2
Kwan_186
LauxdaCosta_OD_3
Lee_4
Leong_24
Leong_RISK_29
Long_RES_10
Maertzdorf_15
Maertzdorf_4
Maertzdorf_OD_100
Natarajan_7
PennNich_RISK_6
Qian_OD_17
Rajan_HIV_5
Roe_3
Roe_OD_4
Sambarey_HIV_10
Singhania_OD_20
Sivakumaran_11
Sloot_HIV_2
Suliman_4
Suliman_RISK_2
Suliman_RISK_4
Sweeney_OD_3
Tabone_OD_11
Tabone_RES_25
Tabone_RES_27
Thompson_9
Thompson_FAIL_13
Thompson_RES_5
Tornheim_71
Tornheim_RES_25
Vargas_18
Vargas_42
Verhagen_10
Walter_51
Walter_PNA_119
Walter_PNA_47
Zak_RISK_16
Zhao_NANO_6
AUC Table
set.seed(0)
tableAUC(ssgsea_res,
annotationColName = "Disease",
signatureColNames = names(TBsigs),
num.boot = 100,
pb.show = FALSE)AUC Boxplots
set.seed(0)
compareBoxplots(ssgsea_res, annotationColName = "Disease",
signatureColNames = names(TBsigs),
pb.show = FALSE, fill.col = "blue",
rotateLabels = TRUE)
ROC plots
signatureROCplot_CI(inputData = ssgsea_res,
signatureColNames = names(TBsigs),
annotationColName = "Disease")##
|
| | 0%
|
|= | 1%
|
|== | 3%
|
|=== | 4%
|
|==== | 5%
|
|==== | 6%
|
|===== | 8%
|
|====== | 9%
|
|======= | 10%
|
|======== | 12%
|
|========= | 13%
|
|========== | 14%
|
|=========== | 15%
|
|============ | 17%
|
|============= | 18%
|
|============= | 19%
|
|============== | 21%
|
|=============== | 22%
|
|================ | 23%
|
|================= | 24%
|
|================== | 26%
|
|=================== | 27%
|
|==================== | 28%
|
|===================== | 29%
|
|====================== | 31%
|
|====================== | 32%
|
|======================= | 33%
|
|======================== | 35%
|
|========================= | 36%
|
|========================== | 37%
|
|=========================== | 38%
|
|============================ | 40%
|
|============================= | 41%
|
|============================== | 42%
|
|=============================== | 44%
|
|=============================== | 45%
|
|================================ | 46%
|
|================================= | 47%
|
|================================== | 49%
|
|=================================== | 50%
|
|==================================== | 51%
|
|===================================== | 53%
|
|====================================== | 54%
|
|======================================= | 55%
|
|======================================= | 56%
|
|======================================== | 58%
|
|========================================= | 59%
|
|========================================== | 60%
|
|=========================================== | 62%
|
|============================================ | 63%
|
|============================================= | 64%
|
|============================================== | 65%
|
|=============================================== | 67%
|
|================================================ | 68%
|
|================================================ | 69%
|
|================================================= | 71%
|
|================================================== | 72%
|
|=================================================== | 73%
|
|==================================================== | 74%
|
|===================================================== | 76%
|
|====================================================== | 77%
|
|======================================================= | 78%
|
|======================================================== | 79%
|
|========================================================= | 81%
|
|========================================================= | 82%
|
|========================================================== | 83%
|
|=========================================================== | 85%
|
|============================================================ | 86%
|
|============================================================= | 87%
|
|============================================================== | 88%
|
|=============================================================== | 90%
|
|================================================================ | 91%
|
|================================================================= | 92%
|
|================================================================== | 94%
|
|================================================================== | 95%
|
|=================================================================== | 96%
|
|==================================================================== | 97%
|
|===================================================================== | 99%
|
|======================================================================| 100%
Separate ROC plots
for (i in names(TBsigs)){
cat("#####" , i, "\n")
print(signatureROCplot_CI(inputData = ssgsea_res,
signatureColNames = i,
annotationColName = "Disease",
name = paste("ROC plot,", i, sep = " ")))
cat("\n\n")
}Anderson_42
|
| | 0% |
|======================================================================|
100%
Anderson_OD_51
|
| | 0% |
|======================================================================|
100%
Berry_393
|
| | 0% |
|======================================================================|
100%
Berry_OD_86
|
| | 0% |
|======================================================================|
100%
Blankley_380
|
| | 0% |
|======================================================================|
100%
Blankley_5
|
| | 0% |
|======================================================================|
100%
Bloom_OD_144
|
| | 0% |
|======================================================================|
100%
Bloom_RES_268
|
| | 0% |
|======================================================================|
100%
Bloom_RES_558
|
| | 0% |
|======================================================================|
100%
Chen_5
|
| | 0% |
|======================================================================|
100%
Chen_HIV_4
|
| | 0% |
|======================================================================|
100%
Darboe_RISK_11
|
| | 0% |
|======================================================================|
100%
Dawany_HIV_251
|
| | 0% |
|======================================================================|
100%
Duffy_23
|
| | 0% |
|======================================================================|
100%
Esmail_203
|
| | 0% |
|======================================================================|
100%
Esmail_82
|
| | 0% |
|======================================================================|
100%
Esmail_OD_893
|
| | 0% |
|======================================================================|
100%
Estevez_133
|
| | 0% |
|======================================================================|
100%
Estevez_259
|
| | 0% |
|======================================================================|
100%
Francisco_OD_2
|
| | 0% |
|======================================================================|
100%
Gjoen_10
|
| | 0% |
|======================================================================|
100%
Gjoen_7
|
| | 0% |
|======================================================================|
100%
Gliddon_2_OD_4
|
| | 0% |
|======================================================================|
100%
Gliddon_HIV_3
|
| | 0% |
|======================================================================|
100%
Gliddon_OD_3
|
| | 0% |
|======================================================================|
100%
Gliddon_OD_4
|
| | 0% |
|======================================================================|
100%
Gong_OD_4
|
| | 0% |
|======================================================================|
100%
Heycken_FAIL_22
|
| | 0% |
|======================================================================|
100%
Hoang_OD_13
|
| | 0% |
|======================================================================|
100%
Hoang_OD_20
|
| | 0% |
|======================================================================|
100%
Hoang_OD_3
|
| | 0% |
|======================================================================|
100%
Huang_OD_13
|
| | 0% |
|======================================================================|
100%
Jacobsen_3
|
| | 0% |
|======================================================================|
100%
Jenum_8
|
| | 0% |
|======================================================================|
100%
Kaforou_27
|
| | 0% |
|======================================================================|
100%
Kaforou_OD_44
|
| | 0% |
|======================================================================|
100%
Kaforou_OD_53
|
| | 0% |
|======================================================================|
100%
Kaul_3
|
| | 0% |
|======================================================================|
100%
Kulkarni_HIV_2
|
| | 0% |
|======================================================================|
100%
Kwan_186
|
| | 0% |
|======================================================================|
100%
LauxdaCosta_OD_3
|
| | 0% |
|======================================================================|
100%
Lee_4
|
| | 0% |
|======================================================================|
100%
Leong_24
|
| | 0% |
|======================================================================|
100%
Leong_RISK_29
|
| | 0% |
|======================================================================|
100%
Long_RES_10
|
| | 0% |
|======================================================================|
100%
Maertzdorf_15
|
| | 0% |
|======================================================================|
100%
Maertzdorf_4
|
| | 0% |
|======================================================================|
100%
Maertzdorf_OD_100
|
| | 0% |
|======================================================================|
100%
Natarajan_7
|
| | 0% |
|======================================================================|
100%
PennNich_RISK_6
|
| | 0% |
|======================================================================|
100%
Qian_OD_17
|
| | 0% |
|======================================================================|
100%
Rajan_HIV_5
|
| | 0% |
|======================================================================|
100%
Roe_3
|
| | 0% |
|======================================================================|
100%
Roe_OD_4
|
| | 0% |
|======================================================================|
100%
Sambarey_HIV_10
|
| | 0% |
|======================================================================|
100%
Singhania_OD_20
|
| | 0% |
|======================================================================|
100%
Sivakumaran_11
|
| | 0% |
|======================================================================|
100%
Sloot_HIV_2
|
| | 0% |
|======================================================================|
100%
Suliman_4
|
| | 0% |
|======================================================================|
100%
Suliman_RISK_2
|
| | 0% |
|======================================================================|
100%
Suliman_RISK_4
|
| | 0% |
|======================================================================|
100%
Sweeney_OD_3
|
| | 0% |
|======================================================================|
100%
Tabone_OD_11
|
| | 0% |
|======================================================================|
100%
Tabone_RES_25
|
| | 0% |
|======================================================================|
100%
Tabone_RES_27
|
| | 0% |
|======================================================================|
100%
Thompson_9
|
| | 0% |
|======================================================================|
100%
Thompson_FAIL_13
|
| | 0% |
|======================================================================|
100%
Thompson_RES_5
|
| | 0% |
|======================================================================|
100%
Tornheim_71
|
| | 0% |
|======================================================================|
100%
Tornheim_RES_25
|
| | 0% |
|======================================================================|
100%
Vargas_18
|
| | 0% |
|======================================================================|
100%
Vargas_42
|
| | 0% |
|======================================================================|
100%
Verhagen_10
|
| | 0% |
|======================================================================|
100%
Walter_51
|
| | 0% |
|======================================================================|
100%
Walter_PNA_119
|
| | 0% |
|======================================================================|
100%
Walter_PNA_47
|
| | 0% |
|======================================================================|
100%
Zak_RISK_16
|
| | 0% |
|======================================================================|
100%
Zhao_NANO_6
|
| | 0% |
|======================================================================|
100%
